These notes are mostly just some important points w/o much context.
I'm starting from d2l.ai as it's the most comprehensive resource I could find but I'm sure I'll branch out into reading though other sources on the way.
- Introduction
- You can think of the parameters as knobs that we can turn, manipulating the behavior of the program. Fixing the parameters, we call the program a model.
- The set of all distinct programs (input-output mappings) that we can produce just by manipulating the parameters is called a family of models.
- And the meta-program that uses our dataset to choose the parameters is called a learning algorithm.
- where we try to predict a designated unknown label based on known inputs given a dataset consisting of examples for which the labels are known, is called supervised learning
- some core components that will follow us around, no matter what kind of machine learning problem we take on:
- The data that we can learn from.
- Generally, we are concerned with a collection of examples. In order to work with data usefully, we typically need to come up with a suitable numerical representation. Each example (or data point, data instance, sample) typically consists of a set of attributes called features (sometimes called covariates or inputs), based on which the model must make its predictions. In supervised learning problems, our goal is to predict the value of a special attribute, called the label (or target), that is not part of the model’s input.
- A model of how to transform the data.
- By model, we denote the computational machinery for ingesting data of one type, and spitting out predictions of a possibly different type.
- An objective function that quantifies how well (or badly) the model is doing.
- An algorithm to adjust the model’s parameters to optimize the objective function.
- The data that we can learn from.
- Kinds of Machine Learning problems
- Supervised Learning
- Regression (ex–predicting arbitrary numbers)
- Classification
- Tagging
- Search
- Recommender Systems
- Sequence Learning
- Tagging and Parsing
- Speech Recognition
- Text to Speech
- Translation
- Unsupervised and Self-Supervised Learning
- ...
- Supervised Learning